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| 001 | 88954815 | ||
| 003 | FRCYB88954815 | ||
| 005 | 20250108003925.0 | ||
| 006 | m o d | ||
| 007 | cr un | ||
| 008 | 250108s2024 fr | o|||||0|0|||eng d | ||
| 020 | _a9781835461969 | ||
| 035 | _aFRCYB88954815 | ||
| 040 |
_aFR-PaCSA _ben _c _erda |
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| 100 | 1 | _aTheobald, Oliver | |
| 245 | 0 | 1 |
_aMachine Learning with Python _bUnlocking AI Potential with Python and Machine Learning _c['Theobald, Oliver'] |
| 264 | 1 |
_bPackt Publishing _c2024 |
|
| 300 | _a p. | ||
| 336 |
_btxt _2rdacontent |
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| 337 |
_bc _2rdamdedia |
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| 338 |
_bc _2rdacarrier |
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| 650 | 0 | _a | |
| 700 | 0 | _aTheobald, Oliver | |
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_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88954815 _qtext/html _a |
| 520 | _aUnlock the secrets of data science and machine learning with our comprehensive Python course, designed to take you from basics to complex algorithms effortlesslyKey FeaturesNavigate through Python's machine learning libraries effectivelyLearn exploratory data analysis and data scrubbing techniquesDesign and evaluate machine learning models with precisionBook DescriptionThe course starts by setting the foundation with an introduction to machine learning, Python, and essential libraries, ensuring you grasp the basics before diving deeper. It then progresses through exploratory data analysis, data scrubbing, and pre-model algorithms, equipping you with the skills to understand and prepare your data for modeling. The journey continues with detailed walkthroughs on creating, evaluating, and optimizing machine learning models, covering key algorithms such as linear and logistic regression, support vector machines, k-nearest neighbors, and tree-based methods. Each section is designed to build upon the previous, reinforcing learning and application of concepts. Wrapping up, the course introduces the next steps, including an introduction to Python for newcomers, ensuring a comprehensive understanding of machine learning applications.What you will learnAnalyze datasets for insightsScrub data for model readinessUnderstand key ML algorithmsDesign and validate modelsApply Linear and Logistic RegressionUtilize K-Nearest Neighbors and SVMsWho this book is forThis course is ideal for aspiring data scientists and professionals looking to integrate machine learning into their workflows. A basic understanding of Python and statistics is beneficial. | ||
| 999 |
_c79064 _d79064 |
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